0
0.0
May 29, 2023
05/23
May 29, 2023
by
Saeed Behzadi
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Today, traffic is one of the biggest problems of urban management. There are two general methods for traffic management, soft and hard methods. In the hard method, physical changes are applied to the road network, and in the soft method, the existing conditions are optimized. Traffic forecasting is one of the soft methods for traffic management. Traffic forecasting is usually done based on the time of existing traffic conditions, while the effect of location and neighborhood, which is one of...
Topics: Geographic information system, Traffic, Prediction, Traffic map
2
2.0
May 22, 2023
05/23
May 22, 2023
by
Pali Sahu
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Organic matter in water is assessed through Chemical oxygen demand (COD). COD prediction utilizing Data driven technique (DDT) has shown to be promising and may be utilized as supplemental techniques due to the time-consuming procedure and nonlinear correlations between the factors. The current study aims to determine how well three different DDT, namely Artificial Neural Network (ANN), Multi-Gene Genetic Programming (MGGP), and Model Tree (M5T), can estimate the concentration of COD in water...
Topics: ANN, MGGP, Soft computing, Modelling, Water quality, Chemical oxygen demand
4
4.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Yashar Dadrasajirlou
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Evapotranspiration is a non-linear and complex phenomenon requiring different climatic variables for accurate estimation. In this study, the performance of several artificial intelligence models in estimating the amount of monthly reference evapotranspiration was investigated. Babolsar and Ramsa regions located in the north of Iran were selected as case study models proposed in this study: artificial neural network (ANN), least square support vector machines (LSSVM), and M5 tree models. The...
Topics: Reference evapotranspiration, LSSVM, ANN, M5 Tree, Babolsar, Ramsar
2
2.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Giuseppe Guido
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Road safety is an important subject to study in both the technical and academic fields of road transportation. In recent years, there has been a significant rise in the number of studies that look at how intelligent transportation systems (ITS) can be used and what role it plays in making roads safer in different countries. Nevertheless, there are still relatively few in-depth quantitative and qualitative analyses published on the topic of ITS's role in ensuring road safety. For this purpose,...
Topics: Road safety, Road transportation, Intelligent transportation systems, WoS
3
3.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Mohammad Reza Mashayekhi
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Using a tuned mass damper (TMD) is one of the passive methods of controlling structural vibrations. This energy absorption system has a mass, a spring, and a damper attaching to the main structure and vibrating with it, reducing the dynamic response of the structure by preventing the intensification. Therefore, finding optimal parameters is one of the main essential issues in the study and design of tuned mass dampers. This study investigates the optimization of parameters of an adjusted mass...
Topics: Tuned mass damper, Optimal parameters, Particle swarm optimization (PSO) algorithm, Whale...
6
6.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Febrina Vienna Soulisa
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A well-planned layout will contribute to saving time and site congestion as well as minimize travel distance, material handling effort, and operational cost. However, most of developed mathematical optimization procedures only work for small-scale problems and often falls into either local or global optima which do not guarantee the further convergence. Therefore, this study is motivated to propose a Hybrid Ant Lion Optimizer (ALO) algorithm inspired by ant lions’ predatory behavior,...
Topics: Site layout, Optimization, Hybrid Ant Lion Optimizer (ALO), Artificial intelligent
2
2.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Shalini
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This paper presents the use of data-driven models, namely Gene expression programming (GEP), M5 model tree (M5-TREE), Multivariate adaptive regression spline (MARS), and Adaptive neuro-fuzzy inference system (ANFIS) to predict bridge pier scour depth. Only 213 data sets of the live bed conditions from laboratory tests and field data measurements were considered for the present analysis. The gamma test has been performed to determine the ideal input combinations for model development. Five main...
Topics: Data-driven model, Gamma test, Scour, Live-bed
2
2.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Ali Reza Ghanizadeh
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Chemical stabilization is used to enhance and increase the strength characteristics of soft and problematic soils. In this research, Gaussian Process Regression (GPR) is employed to estimate the unconfined compressive strength (UCS) and the Young’s modulus (E) of lean clay soils stabilized with iron ore mine tailing (IOMT) and hydrated lime (HL) percentage. In this regard, four inputs including the moisture content (MC), IMOT percentage, HL percentage, and curing time (CT) were used. The...
Topics: Lean clay stabilization, Iron ore mine tailing, Hydrated lime, Unconfined compressive strength,...
4
4.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Georgios Stavroulakis
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A nonlinear model for the vibration suppression of a smart composite elastic plate using graphical representation involving fuzzy control is presented. The plate follows the von Kármán and Kirchhoff plate bending theories and the oscillations are caused by external transversal loading forces, which are applied directly on it. Two different control forces, one continuous and one located at discrete points, are considered. The mechanical model is spatially discretized by using the time spectral...
Topics: Smart plate model, Spatial discretization, Fuzzy control, Computational algorithm, SIMULINK diagrams
2
2.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Sarat Chandra Nayak
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Manual estimation of compressive strength of concrete (CSC) is time consuming and expensive. Soft computing techniques are found better to statistical methods applied to this problem. However, sophisticated prediction models are still lacking and need to be explored. Extreme learning machine (ELM) is a faster and better learning method for artificial neural networks (ANNs) with solitary hidden layer and has enhanced generalization capacity. This article presents an ELM-based forecast for...
Topics: Compressive strength of cement, ELM, ANN, Back propagation neural network, SVM, ARIMA
5
5.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Nitin Dahiya
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In this research, the number of dowels (horizontal connection) has been determined using support vector machines (SVM), gradient boosting and artificial neural networks (ANN-Multilayer perceptron). Building height, length and thickness of the wall, maximum shear, maximum compressive force and maximum tension were the input parameters while the output parameter was the number of dowels. 1140 machine learning models were used, out of which 814 were used as training datasets and 326 as test...
Topics: Machine learning, Gradient boosting, Support vector machines, Precast concrete structures, Computer...
6
6.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Akbar Shirzad
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Peak ground acceleration (PGA) is a critical parameter in ground-motion investigations, in particular in earthquake-prone areas such as Iran. In the current study, a new method based on particle swarm optimization (PSO) is developed to obtain an efficient attenuation relationship for the vertical PGA component within the northern Iranian plateau. The main purpose of this study is to propose suitable attenuation relationships for calculating the PGA for the Alborz, Tabriz and Kopet Dag faults in...
Topics: Attenuation relationships, Northern Iranian plateau, Optimization, PSO algorithm, Vertical PGA...
2
2.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Danial Jahed Armaghani
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Pile foundations are vastly utilized in construction projects where their capacities (pile bearing capacity, PBC) should be determined in different stages of construction. A highly reliable and accurate prediction model can lead to many advantages, such as reducing the construction cost, shortening the construction timeline, and providing safety construction. Hence, the aim of this study is the developments of statistical and artificial intelligence (AI) models for predicting bearing capacities...
Topics: Deep foundation capacity, Support vector machine, Kernels, Linear and multiple regression
5
5.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Mahesh Patel
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In the construction field, compressive strength is one of the most critical parameters of concrete. However, a significant amount of physical effort and natural raw materials are required to produce concrete. In addition, the curing period of concrete for at least 28 days is a must for attaining the required compressive strength. Various types of industrial and agricultural wastes have been used in concrete to reduce cement consumption and problems due to its production. Therefore, considering...
Topics: Compressive strength, Fly ash, Machine learning, Prediction
6
6.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Biao He
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Compression index is an effective assessment of primary consolidation settlement of clayey soils, but the process of obtaining compression index is time-consuming and laborious. Thus, in the present study, we developed two classical tree-based techniques: random forest (RF) and extreme gradient boosting (XGBoost), to predict the compression index of clayey soils. To establish these two models, we collected an available dataset—including 391 consolidation tests for soils—from previously...
Topics: Compression index, Consolidation, Machine learning, Random forest, Extreme gradient boosting
5
5.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Nhat-Duc Hoang
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The performance and serviceability of asphalt pavements have a direct influence on people's daily lives. Timely detection of pavement cracks is crucial in the task of periodic pavement survey. This paper proposes and verifies a novel computer vision-based method for recognizing pavement crack patterns. Image processing techniques, including Gaussian steerable filters, projection integrals, and image texture analyses, are employed to characterize the surface condition of asphalt pavement roads....
Topics: Computer vision, Image processing, Pavement cracks, Light gradient boosting machine, Deep neural...
9
9.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Behrang Beiranvand
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The implementation of large dam construction projects, despite the positive economic and social effects on the region, may endanger the development of the region with long-term negative effects. Therefore, it seems necessary to pay attention to this issue to reduce the negative effects of large dam construction projects and to consider them in the evaluation of benefits and costs for policy and codified planning in the water resources sector. In this research, Shannon's entropy-TOPSIS...
Topics: Eyvashan dam, FIS-FMEA, Risk priority number (RPN), TOPSIS, Entropy weight
2
2.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Mohammad Reza Esfahani
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In this paper, the location of single, double, and triple damage scenarios in reinforced concrete (RC) beams are assessed using the Wavelet transform coefficient. To achieve this goal, the numerical models of RC concrete beams were conducted based on the experimental specimens. The mode shapes and corresponding modal curvatures of the RC beam models in damaged and undamaged status were considered as input signals in Wavelet transform. By considering the Wavelet coefficient as damage index,...
Topics: Damage location, Wavelet transform, Noisy contaminated condition, Modal curvature, RC beams
3
3.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Hojat Karami
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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Evaporation pans (EP) are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. The purpose of this study is to evaluate the efficiency of the Long- Short Term Memory (LSTM) model to estimate evaporation from a pan and compare it with the Multilayer Perceptron (MLP) model in Semnan and Garmsar. For this purpose, daily...
Topics: Evaporation pan, DEEP-LSTM, MLP, Meteorological data, Semnan, Garmsar
3
3.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Biao He
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As massive tunneling projects become more and more popular, predicting the performance of Tunnel Boring Machine (TBM) has been a problem that arose recently. A TBM is a modern piece of machinery that is specially assembled to excavate a tunnel more efficiently and safely. However, the performance of TBM is very difficult to estimate due to the different geological formations and geotechnical factors. This research aims to predict the penetration rate (PR) of TBM utilizing statistical and...
Topics: ANN, GMDH, TBM performance, Penetration rate
3
3.0
Apr 23, 2023
04/23
Apr 23, 2023
by
B S Keerthi Gowda
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Agricultural waste materials are increasingly being used as partial replacements for cement in concrete. Several experimental studies are available to evaluate the mechanical properties of plastic waste reinforced concrete but there are limited evaluations on agricultural waste material. In this study, an attempt is made to investigate the compressive strength of Corn Cob Ash (CCA) concrete at different replacement levels by implementing an Artificial Neural Network (ANN). As the percentage of...
Topics: Soft-computing techniques, Levenberg-Marquardt, Agriculture waste, Construction materials,...
3
3.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Somayeh Emami
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In arid and semi-arid areas, optimization and strategic planning of water delivery through an optimal and intelligently designed reservoir supply system is a primary task for water resources management. In this regard, the election algorithm (EA) is presented to estimate the optimal storage capacity of the Mahabad dam located in northwest Iran. EA is an intelligent iterative population-based algorithm that has recently been introduced for dealing with different optimization purposes. The...
Topics: Election algorithm, Continuous genetic algorithm, Optimization, Reservoir storage, Mahabad dam
6
6.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Danial Jahed Armaghani
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Prediction of bus travel time is a key component of an intelligent transportation system and has many benefits for both service users and providers. Although there is a rich literature on bus travel prediction, some limitations can still be observed. First, high-frequency and low-frequency bus routes have different characterizations in both operational and passenger behavior aspects. Therefore, it is highly expected that bus travel time prediction methods for different frequencies must have...
Topics: Machine learning, Tree-based models, High-frequency route, Low-frequency service, Travel time...
4
4.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Nand Kumar Tiwari
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The gabion weirs serve the same functions that their counterpart impervious weirs do. However, they have the advantage of being eco-friendly, more stable, and economical in low to medium-head cases. Dissolved oxygen is one of the major determinants for the assessment of the purity of water. The purpose of the present work is to illustrate the comparison of multiple linear regression (MLR), neural network (NN), neuro-fuzzy system (NFS), deep neural network (DNN), and reported empirical models...
Topics: Gabion weir aeration- performance efficiency, Neural network, Neuro-fuzzy, Deep neural network,...
2
2.0
Apr 23, 2023
04/23
Apr 23, 2023
by
Sepideh Vafaie
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This study presents a new approach based on shearlet transform for the first time to detect damages, and compare it with the wavelet, Laplacian pyramid, curvelet, and contourlet transforms to specify different types of defects in plate structures. Wavelet and Laplacian pyramid transforms have inferior performance to detect flaws with different multi-directions, such as curves, because of their basic element form, expressing the need for more efficient transforms. Therefore, some transforms,...
Topics: Wavelet transform, Laplacian pyramid transform, Curvelet transform, Contourlet transform, Shearlet...
Manufactured sand has high potential for replacing natural sand and reducing the negative impact of the construction industry on the environment. This paper aims at developing a novel deep learning-based approach for estimating the compressive strength of manufactured-sand concrete. The deep neural networks are trained by the advanced optimizers of Root Mean Squared Propagation, Adaptive Moment Estimation, and Adaptive Moment Estimation with Nesterov momentum (Nadam). In addition, the...
Topics: Compressive strength, Manufactured-sand concrete, Deep learning, Neural network, Advanced optimizers
India is the 7th largest country by area and 2nd most populated country in the world. The reports prepared by IQAir revels that India is 3rd most polluted country after Bangladesh and Pakistan, on the basis of fine particulates (PM2.5) concentration for the year 2020. In this article, the quality of air in six Indian cities is predicted using data-driven Artificial Neural Network. The data was taken from the 'Kaggle' online source. For six Indian cities, 6139 data sets for ten contaminants...
Topics: ANN, Smart cities, Air pollution, Air quality prediction, Artificial intelligence
In recent years, new ideas and solutions have been proposed by scientists and researchers to control the response of structures against seismic excitations. In this article, The Optimization of semi-active control systems using MR dampers has been studied to reduce the structure's response under earthquake forces. For this purpose, three frames of five, eight, and eleven stories have been examined as numerical examples. A multi-objective optimization approach based on the NSGA-II algorithm is...
Topics: Semi-active control, MR damper, Multi-objective optimization, Fuzzy logic, NSGA-II algorithm
An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete with respect to the percentage of mineral admixture and percentage of crystalline admixture. To accomplish this, an experimental database of 100 samples is compiled from the literature and utilized to find the best ANN architecture. The main aim of this paper was to predict the strength properties of self-healing concrete (SHC) with crystalline admixture and...
Topics: Rootmean square error, Artificial neural network, SHC, Crystalline admixture, Mineral admixture
Textile reinforced mortars (TRMs) are new composite materials which were considered as a proper alternative for fiber reinforced polymers (FRPs) to strengthen various structural elements. In comparison to FRPs, the TRMs have more fire resistance, more environmental consistency and are safer the structural elements because of their better bond to substrate and various failure modes. There are a lot of existing models to calculate the bond strength between TRMs and concrete substrate. But, most...
Topics: Textile reinforced mortar (TRM), Fiber reinforced polymer (FRP), Bond strength, Calibration...
The Pavement Condition Index (PCI) is one of the most critical pavement performance indicators and ride quality. This study aims to develop PCI models based on pavement distress parameters using conventional technique and artificial neural network (ANN) technique across two climate regions in the U.S. and Canada. The long-term pavement performance (LTPP) database was used to obtain pavement distress data, including pavement age, rutting, fatigue cracking, block cracking, longitudinal cracking,...
Topics: Artificial neural network, Pavement distresses, Pavement condition index (PCI), Long-term pavement...
Most of the Indian black topped roads have been damaged due to adverse weather and heavy load distresses. Many researchers have focused on improving durability of Hot Mix Asphalt (HMA) pavements. The factors influencing durability of HMA are Binder content, Combined aggregate gradation, type of Filler and addition of Fiber. In order to optimize the combination of variables used in HMA mix design, Grey relational analysis by using Taguchi technique is used, where many parameters can be analyzed...
Topics: Optimization, HMA Fiber, Grey Taguchi, Orthogonal array, ANOVA
The aim of the present study is to apply machine learning technique to predict the ultimate bearing capacity of the rectangular footing on layered sand under inclined loading. For this purpose, a total 5400 data based on the finite element method for the rectangular footing on layered sand under inclined loading were collected from the literature to develop the machine learning model. The input variables chosen were the thickness ratio (0.00 to 2.00) of the upper dense sand layer, embedment...
Topics: Bearing capacity, Layered sand, Rectangular footing, Inclined load, Machine learning technique
etting the position of the instrument in starting traverse and staking out surveying can be very helpful for the surveyors. The most common method is the placement of the instrument on the known point, then those surveys are possible to be accomplished. This research is aimed to develop a new method and procedure to get x, y, and z values of the unknown position of the instrument based on two known points and the law of cosines. The method of this research is the implementation of the law of...
Topics: Law of cosines, Traverse measurement, Position, Coordinate, Simulation, Precision, Accuracy
Dam construction projects among the most extensive and most expensive projects are considered. It is always appropriate and optimal for such concrete structures to reduce the volume of concrete and consequently reduce construction costs is essential. In this study, invasive weed optimization software GNU octave, dimensions of concrete gravity dam Koyna located in India optimized stability constraints. For this purpose, a cross-section with a length unit consists of eight geometric parameters as...
Topics: Invasive weeds, Optimization method, Koyna concrete gravity dam, Optimal, Dimensions of dam
A lot of fieldwork is required to assess the safe bearing capacity (SBC) of fine-grained soil using IS Code, along with performing shear parameters to determine angle of internal friction and cohesion. Standard penetration tests are conducted in order to obtain N-value of soil, and evaluating atterberg limits and dry soil density. Here, it is proposed that Adaptive Neuro-Fuzzy Inference System(ANFIS) is adopted to predict fine-grained soil's safe bearing capacity. For this, input parameters...
Topics: Safe bearing capacity (SBC), Liquid limit, Adaptive neuro-fuzzy inference system (ANFIS), Dry...
This paper presents a statistical correlation analysis of peak ground acceleration to peak ground velocity ratio (A/V) and other ground motion intensity measures (IMs) for Iran’s data. A/V is an important parameter that can significantly affect nonlinear structural responses. Findings from this study provide beneficial insights into selecting suitable parameters for characterizing earthquake ground motions. The studied database included 2053 strong ground motion records with the moment...
Topics: Ground motion intensity measures, Near-field earthquakes, Far-field earthquakes A/V
The current study uses machine learning techniques such as Random Forest Regression (RFR), Artificial Neural Networks (ANN), Support Vector Machines Ploy kernel (SVMP), Support Vector Machines Radial Basis Function Kernel (SVMRBK), and M5P model tree (M5P) to estimate unconfined compressive strength of organic clay stabilized with fly ash. The unconfined compressive strength of stabilized clay was computed by considering the different input variables namely i) the ratio of Cao to Sio2, ii)...
Topics: Unconfined compressive strength, Organic clay, Fly ash, Random Forest Regression, Artificial Neural...
Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO...
Topics: Optimization, Grey wolf optimizer (GWO), Elephant herding optimization (EHO), Convergence speed,...
Liquefaction occurs when saturated, non-cohesive soil loses strength. This phenomenon occurs as the water pressure in the pores rises and the effective stress drops because of dynamic loading. Liquefaction potential is a ratio for the factor of safety used to figure out if the soil can be liquefied, and liquefaction-induced settlements happen when the ground loses its ability to support construction due to liquefaction. Traditionally, empirical and semi-empirical methods have been used to...
Topics: Settlements, Adaptive neuro-fuzzy inference system (ANFIS), Liquefaction, Liquefaction-potential,...
A reliability and safety assessment for a bunch of listed schools can be challengeable for experts by the checklist system for safety reports. This paper aimed to respond to this challenge by merging the edges of failure 'EoF' and the technique for order of preference by similarity to ideal solution 'TOPSIS' to achieve an integrative approach for educational environment safety assessment. The qualitative assessment was implemented to detect safety faults in the case study area based on the...
Topics: TOPSIS, Edges of failure, Integration, Jeopardous triangle
Climate change has affected many sectors in the world. Therefore, the Prediction of climatic factors is essential in case to achieve sustainability in human life. Rainfall prediction is also important as the agricultural sector depends on rainfall, and human life depends on agricultural products. This study presents the Standardized Precipitation Index (SPI) prediction using the adaptive neuro-fuzzy inference system (ANFIS). Various models (6 nos.) with different combinations of Rainfall and...
Topics: ANFIS, Drought, Forecasting, SPI, Surat
Nowadays, technology has advanced, particularly in machine learning which is vital for minimizing the amount of human work required. Using machine learning approaches to estimate concrete properties has unquestionably triggered the interest of many researchers across the globe. Currently, an assessment method is widely adopted to calculate the impact of each input parameter on the output of a machine learning model. This paper evaluates the capability of various machine learning methodologies...
Topics: Concrete materials management, Compressive strength, Partial dependence analysis, Machine learning,...
Members with GFRP bars exhibit different behavior from those with steel bars due to the brittle and low elastic modulus of the GFRP bars. However, there are limited studies considering the optimum design of reinforced concrete members with GFRP bars compared with extensive studies for reinforced concrete members with steel bars. This study highlighted the performance of reinforced concrete beams with GFRP bars considering the optimum design. The behavioral flexural resistances involving...
Topics: Optimization, GFRP bars, Reinforced concrete, Beams, Genetic algorithm
Manual inspection of cracks on concrete surfaces requires wholesome knowledge and depends entirely on the expertise and capabilities of the inspector. This study proposes the use of a simple Convolutional Neural Network (CNN) for automatic crack detection. A comparative approach for Automated Crack Detection is presented between Feed-Forward Fully Connected Neural Networks and CNN, focusing on the primary hyperparameters affecting the accuracy of both systems. An inclination towards CNN is...
Topics: Deep learning, Computer vision, Maintenance and rehabilitation, Artificial neural network
Corrosion is an important phenomenon that occurs at concrete-rebar interface and affects the life of structures in coastal environments. Fe-600 grade steel is used in India for construction purposes especially in seismic zones. Hence, the corrosion of the rebars and its optimization is necessary to increase the lifetime of structures. In this regard, the present investigation examines the applicability of electroless Ni-P based ternary coatings as candidates for corrosion protection and obtains...
Topics: Ni-W-P, Ni-Cu-P, Electroless, Rebar, Grey-fuzzy optimization, Corrosion
Liquefaction is one of the disasters caused due to earthquake. In 1999, Chi-Chi, Taiwan, earthquake is an example of liquefaction prone disasters induced due to Mw 7.6 earthquake. This becomes major cause for prediction of the liquefaction in the soil with respect to geotechnical property. In this paper, we have use Artificial Neural Networks (ANN) model based on Resilient Back propagation (Rprop), Decision tree model (DT) and classifier are C 4.5 and Random Forest is done for comparing the...
Topics: Resilient back propagation (Rprop), C4.5 Algorithm, Random forest algorithm, Liquefaction
Protein-Protein Inter-action Networks are dynamic in reality; i.e. Inter-actions among different proteins may be ineffective in different circumstances and times. One of the most crucial parameters in the conversion of a static network into a temporal graph is the well-tuning of transformation threshold. In this part of the article, using additional data, like gene expression data in different times and circumstances and well-known protein complexes, it is tried to determine an appropriate...
Topics: P-P interaction networks, Dynamic networks, SSPCO, Graph partitioning, Gene expression
As heavy rainfall can lead to several catastrophes; the prediction of rainfall is vital. The forecast encourages individuals to take appropriate steps and should be reasonable in the forecast. Agriculture is the most important factor in ensuring a person's survival. The most crucial aspect of agriculture is rainfall. Predicting rain has been a big issue in recent years. Rainfall forecasting raises people's awareness and allows them to plan ahead of time to preserve their crops from the...
Topics: ARIMA, ETS, Holt-winters model, Time series
In the 1993 AASHTO flexible pavement design equation, the structural number (SN) cannot be calculated explicitly based on other input parameters. Therefore, in order to calculate the SN, it is necessary to approximate the relationship using the iterative approach or using the design chart. The use of design chart reduces the accuracy of calculations and, on the other hand, the iterative approach is not suitable for manual calculations. In this research, an explicit equation has been developed...
Topics: Explicit equation, Flexible pavements, AASHTO design guide, Response surface methodology (RSM
Jamuna river is a morphologically very dynamic river. It carries a vast sediment load from the erosive foothills of Himalaya mountain. The length of the Jamuna River is 220 km. For this research work Jamalpur district is selected to assess morphological changes using hydrodynamic, Artificial intelligence and google satellite images. First, the hydrodynamic model was calibrated and validated at Kazipur station for the years 2018 and 2019 respectively. Then, left overbank maximum discharge, water...
Topics: HEC-RAS, Bank erosion, Google satellite images, Machine learning and deep learning technique
The floods of 2018 and 2019 have underlined the urgent need for development and implementation of efficient and robust flood forecasting models for the major rivers in the State of Kerala, India. In this paper, the development and application of two hourly flood forecasting models are presented – one using Support Vector Machine (SVM) and the other based on hybrid wavelet-support vector machine (WSVM). The study was performed on the Achankovil River in Kerala. Wavelet technique was used to...
Topics: Artificial neural networks, Denoising, Peak discharge, Performance rating criteria, Support vector...
In this contemporary technology epoch, material utilization is crucial concerning saving energy demand. One of the thinking points of the interest domain is weight reduction. The highest strength to weight ratio criterion of the welded joint has enthralled keenness in virtually all areas where heft reduction is indispensable. Lightweight materials and their joining processes are also a recent point of research demands in the manufacturing industries. Friction Stir Welding (FSW) is one of the...
Topics: FSW AA6061, Process parameters, Machine learning, Python, Supervised learning
The groundwater is the main source of domestic and agricultural purposes in the arid and semi-arid regions where the surface water availability is limited. To protect and manage the groundwater system effectively, a thorough knowledge and understanding of groundwater quality and application of computational methods to simulate the complex and nonlinear groundwater system are paramount necessary. Generally, three types of models such as physically based model, conceptual models and Blackbox...
Topics: Artificial neural network, Fuzzy logic, Groundwater quality, Supervised machine learning, WQI, Soft...
Freezing is one of the most effective natural and environmental factors on the physical and mechanical characteristics of dimension stones. Since, freezing is a destructive agent, thus causes the undesirable stone conditions and reduces quality and its efficiency. This study, it was aimed to evaluate and rank the dimension stones according to their changes in physical and mechanical properties due to freezing conditions. For this purpose, 14 rock types of the most widely used dimension stones...
Topics: Freezing–thawing, Dimension stones, PROMETHEE, PSO algorithm, Clustering
To provide lateral resistance in structures as well as buildings, there are some types of structural systems such as shear walls. The utilization of lateral loads occurs on a plate on the wall's vertical dimension. Conventionally, these sorts of loads are transferred to the wall collectors. There is a significant resistance between concrete shear walls and lateral seismic loading. To guarantee the building's seismic security, the shear strength of the walls has to be prognosticated by using...
Topics: Artificial neural network, Genetic algorithm, Group Method of Data Handling, Reinforced concrete...
Claim management describes the process required to eliminate or prevent construction claims from arising and for the expedition handling of claims when they do occur. The present study aimed to identify the factors affecting the claimed design and their ranking. This research is applied and descriptive. The effective factors have been identified by reviewing the claims filed by the contractors of Shiraz Municipality during one year and have been classified according to their nature in the four...
Topics: Claim management, Construction claims, Claim types, Project management
A tunneling project is one of the most significant infrastructure projects. Its implementation requires access to adequate data and use of unique proceedings; hence it has a special position among civil engineering projects. Unexpected and uncertain conditions in tunneling projects lead to an increase of potential risks during project implementation. Identifying and evaluating risks in tunneling projects are considered one of the significant challenges among civil engineers, which can cause...
Topics: Risk assessment, Risk management, Tunneling project, FAHP, FMCDM
In the present study, performance of data-mining methods in modeling and estimating reference crop evapotranspiration (ET o ) is investigated. To this end, different machine learning, including Artificial Neural Network (ANN), M5 tree, Multivariate Adaptive Regression Splines (MARS), Least Square Support Vector Machine (LS-SVM), and Random Forest (RF) are employed by considering different criteria including impacts of climate (eight synoptic stations in humid and dry climates), accuracy,...
Topics: Climate, Reference crop evapotranspiration, Data-mining methods, Uncertainty
Compression Index (CI) is one of the frequently used soil parameters for the determination of possible settlement. In this study, the Compression Index of Marine clay is predicted using Artificial Neural network (ANN). Marine clay samples were collected from eight boreholes located at distance varying from 0.5 Km to 2.5 Km landward from the coastline of Pondicherry. The depth of boring was up to 12m. These samples were used for determining the Plastic Limit (PL), Liquid Limit (LL) and the...
Topics: Compression index, Marine clay, Artificial neural network, Multilinear regression
Reinforced concrete (RC) shear wall is one of the most widely adopted earthquake-resisting structural elements. Accurate prediction of capacity curves of RC shear walls has been of significant importance since it can convey important information about progressive damage states, the degree of energy absorption, and the maximum strength. Decades-long experimental efforts of the research community established a systematic database of capacity curves, but it is still in its infancy to productively...
Topics: Machine learning for capacity curve prediction, Multiple-target regression model, Clus, Shear wall...
The paper presents the prediction of bearing capacity equation of E-shaped footing subjected to a vertical concentric load and resting on layered sand using machine learning techniques and the data used in the analysis has been extracted from finite element modelling of the same footing. The input variables used in the developed neural network model were the bearing capacity of square footing, thickness ratio, friction angle ratio and the output were the bearing capacity of E-shaped footing on...
Topics: Bearing capacity, E-shaped footing, Error metrics, Multi-layer perceptron, Multi-linear regression,...
Metaheuristic algorithms have been widely used to solve structural optimization problems. Despite their powerful search capabilities, these algorithms often require a large number of fitness evaluations. Constructing a machine learning classifier to identify which individuals should be evaluated using the original fitness evaluation is a great solution to reduce the computational cost. However, there is still a lack of a thorough comparison between machine learning classifiers when integrating...
Topics: Evolutionary algorithm, Differential evolution, Surrogate model, Machine learning classifier,...
Buildings account for a major part of Total Energy Consumption (TEC) in comparison to that of industry and other sections. The opening and envelope material can affect their TEC. Accordingly, this paper aims to study the effects of the window to external wall ratio (WWR) and the application of recycled panels as the building envelope on the total energy consumption in a one-floor residential building located in Iran and characterized by a semi-arid climate. To follow the sustainability...
Topics: Wall-to-window ratio, Energy consumption, Design builder, Recycled panels, Artificial neural...
Determining the degree of slope stability is one of the most important steps in the design of open pit mines that are affected by other mining activities. So that the collapse of a part of the wall will lead to irreparable human and compensatory damages. Slope stability is affected by natural factors such as lithology, tectonic regime, rock mass conditions, climatic conditions and design factors including slope angle, slope height, pattern and blasting method. In the present study, using a...
Topics: Slope stability, Sungun copper mine, FDAHP, TOPSIS
This paper implements machine learning (ML) classification algorithms on microstructural chemical maps to predict the constituent phases. Intensities of chemical species (Ca, Al, Si, etc.), and in some cases the nanomechanical properties measured at the corresponding points, form the input to the ML model, which predicts the phase label (LD or HD C-S-H, clinker etc.) belonging to that location. Artificial neural networks (ANN) and forest ensemble methods are used for classification. Confusion...
Topics: Machine learning, Nanoindentation, Chemical mapping, Microstructure, Cement pastes, Ultra-high...
In this research, storey drift has been determined using Deep neural networks (DNN Keras). DNN Keras has various hyper tuning parameters (hidden layer, drop out layer, epochs, batch size and activation function) that make it capable to model complex problems. Building height, number of bays, number of storeys, time period, storey displacement, and storey acceleration were the input parameters while storey drift was the output parameter. The dataset consists of 288 models, out of 197 were used...
Topics: Deep neural network, Modelling, Storey drift, Machine-learning, Computer programming
Regarding different aspects of management of drainage basins and droughts, prediction of evaporation is very important. Evaporation is an essential part of the water cycle and plays an important role in the evaluation of climatic characteristics of any region. The purpose of this research is to predict daily pan evaporation rate of Damghan city using an artificial neural network model. The data applied in this research are daily minimum and maximum temperatures, average relative humidity, wind...
Topics: Pan evaporation, Evaporation prediction, Artificial neural network, Damghan
Crack detection and repair of the cracks in engineering structures is essential to ensure serviceability and durability. Traditionally, cracks are detected by the examiner's visual inspection; as a result, crack detection and estimation of characteristics are greatly dependent on the examiner's personal judgment, which has aided in the repair of various structures and evaluation of the crack phenomenon in previous decades. Due to industrial advancement, the number of engineering structures has...
Topics: Cracks, Durability, Noises, Transverse Cracks, Micro-cracks
Estimation of the seismic retrofit cost (SRC) is a complicated task in construction projects. In this study, the performance of four machine learning algorithms (MLAs), including Random Forest (RF), Extreme Learning Machine (ELM), Classification and Regression Tree (CART), and Multivariate Adaptive Regression Spline (MARS), was examined in estimating SRC values. The total floor area (TFA), number of stories (NS), seismic weight (SW), seismicity (S), soil type (ST), plan configuration (PC), and...
Topics: Seismic retrofit cost, Random forest (RF), Extreme learning machine (ELM), Classification and...
For maintaining healthy streams and rivers, a high concentration of oxygen is desired and hydraulic structures act as natural aerators where oxygen transfer occurs by creating turbulence in the water. Aeration studies of conventional weirs are carried out widely in the past but at the same time, labyrinth weirs, where the weir crest is cranked thereby enhancing their crest length, have got a little notice. The test records were obtained through 180 laboratory observations on nine physical...
Topics: Labyrinth weir, Aeration efficiency, SVM, RF, M5P
In Iran, no detailed information on the amount of erosion, sediment transport, and sedimentation of rivers, and in many cases, there are many differences between measurements. Since the flow regime and consequently the sediment regime in the drainage basins are not constant, estimation of sediment discharge can help estimate the sediment accumulated behind the water structures, especially the dams, and determining the dead volume of reservoirs in the coming months, and by adopting timely...
Topics: Meta-heuristic algorithms, Suspended sediment load, Estimation, Zarinehrood river
In this exploratory study, the shear strength of blended cement concrete made using the hydrated lime (HL) as an admixture was studied. 120 shear strength values were experimentally obtained for several mix ratios at 7, 14, 21, and 28 days. This concrete was put together from water, portland cement (PC), HL, river sand (RS), and granite chippings (GC). 96 of the results were utilized to formulate a Levernberg-Marquardt backpropagation artificial neural network (ANN) for determining the shear...
Topics: Backpropagation, Lime, Shear strength, Concrete
The river is an essential resource of fresh water on the earth, and its management is very challenging. Sedimentation and erosion is a very complex process of the river system. Suspended sediment concentration (SSC) plays a key role in this process. Therefore, water resources planning and management are essential for this. Generally, the sediment concentration estimated by direct measurement, but this process is costly and cannot apply in all rivers. It is essential to develop some technology...
Topics: FFBPNN, ANN, SVM, SSC
A hybrid algorithm is presented that combines strong points of Particle Swarm Optimization (PSO) and Generalized Reduced Gradient (GRG) algorithm to keep a good compromise between exploration and exploitation. The hybrid PSO-GRG quickly approximates the optimum solution using PSO as a global search engine in the first phase of the search process. The solution accuracy is then improved during the second phase of the search process using the GRG algorithm to probe locally for a proper solution(s)...
Topics: Hybrid global-local search engine, Particle swarm optimization (PSO), Generalized reduced gradient...
In this paper, the location and severity of damages in prestressed concrete slabs are assessed using the contourlet transform as a novel signal processing method. To achieve this goal, the numerical models of prestressed concrete slabs were built based on the experimental specimens reported in the previous research works. Then, the single, double, and triple damage scenarios with various geometric shapes (transverse, longitudinal, inclined, and curved slots) at different positions (middle and...
Topics: Contourlet transform, Modal data, Damage localization, Damage severity assessment, Prestressed...
Consumption of crushed granite as coarse aggregate in concrete has led to devastating environmental and ecological consequences. In order to preserve local and urban ecology therefore, substitute aggregate such as naturally occurring stone with the propensity of reducing this problem was studied. Furthermore, artificial Neural Network (ANN) models have become the preferred modeling approach due to their accuracy. Thus, in this paper, MATLAB software was used to develop ANN models for predicting...
Topics: ANN model, Bida natural gravel, Mean square error, MLR, Slump
Evaporation is a complex and nonlinear phenomenon due to the interactions of different climatic factors. Therefore, advanced models should be used to estimate evaporation. In the present study, the Neural Network-Based Group Method of Data Handling was used to estimate and simulate the evaporation rate from the pan in the synoptic station of Garmsar city located in Semnan province, Iran. For this purpose, the daily meteorological data of evaporation, minimum and maximum temperature, wind speed,...
Topics: Pan evaporation, GMDH-NN, Hydrology, Sensitivity analysis, Garmsar
A common structural design optimization problem is weight minimization which is done by choosing a set of variables that represent the structural or the architectural configuration of the system satisfying few design specific criterion. In general, genetic algorithms (GAs) are ideal to be used for unconstrained optimization, so it is required to transform the constrained problem into an unconstrained one. A violation of normalized constraints-based formulation method has been used in the...
Topics: Genetic algorithm, Optimization operators, Discreet design variables, Genotypes
The resilient modulus of different pavement materials is one of the most important parameters for the pavement design using the mechanistic-empirical (M-E) method. The resilient modulus is generally determined by a triaxial test, which is expensive and time-consuming and requires special laboratory facilities. This study aims to develop a model based on the Gaussian Process Regression (GPR) to predict the resilient modulus of stabilized base material with different additives under...
Topics: Resilient modulus, Stabilized base, Wetting and drying cycles, Gaussian process regression
Individuals have been regarded as independent decision makers in majority of transportation analysis. However, agents’ behaviors are usually affected by the interactions among the members in a group; and therefore, individual decision-making paradigms may result in unrealistic outcomes and erroneous interpretations of the results. In light of this, the present study develops discrete choice models in individual and group levels and compares their prediction power in predicting the choice of...
Topics: Group decision-making, Interactions, Escort patterns, School trips
The metropolis of Karachi, with a density of around 4000 persons/km 2 at present, is going through unprecedented urbanization and population growth, which can augment Urban Heat Island (UHI) triggered heat wave impacts on life and living. Here, we investigate skill of Regional Climate Model version 4.7 (RegCM4.7) in simulating 2015 heat wave episode in southern Pakistan by dynamically downscaling ERA-Interim reanalysis at 10km resolution and switching on the urban parameterization in the...
Topics: Karachi heat wave, Urban heat island, RegCM4.7, Heat index
Selecting the propriate place of mineral processing plant is one of the most important steps in setting up it. It depends on several factors that make it a subroutine of multi criteria decision making (MCDM) problem. In this research, locating an optimal site for quarries processing plant, using VIKOR method is studied. Three sites were considered for this purpose and criteria such as transportation, water supply, electricity supply, gas supply, distance to markets, the price of land,...
Topics: Place locating, Processing plant, Multi-criteria decision making, VIKOR
The objective of this paper is to find an alternative to conventional method of concrete mix design. For finding the alternative, 4 machine learning algorithms viz. multi-variable linear regression, Support Vector Regression, Decision Tree Regression and Artificial Neural Network for designing concrete mix of desired properties. The multi-variable linear regression model is just a simplistic baseline model, support vector regression Artificial Neural Network model were made because past...
Topics: Machine learning Linear regression, Support vector regression, Decision tree regression, Artificial...
In this paper, the cost and weight of the reinforcement concrete cantilever retaining wall are optimized using Gases Brownian Motion Optimization Algorithm (GBMOA) which is based on the gas molecules motion. To investigate the optimization capability of the GBMOA, two objective functions of cost and weight are considered and verification is made using two available solutions for retaining wall design. Furthermore, the effect of wall geometries of retaining walls on their cost and weight is...
Topics: Retaining wall optimization, Sensitivity analysis, Gasses Brownian motion optimization, Cost and...
The main contribution of this study is to open a discussion regarding the structural optimization associated with the cost efficiency and structural reliability sufficiency consideration. To do so, several various optimization approaches are investigated to deliberate both cost and reliability concerns. Particularly, particle swarm optimization is highlighted as a reliable optimization approach. Accordingly, an illustrative example is rendered to compare the feasibility of the considered...
Topics: Particle swarm optimization (PSO), 2-D truss, Finite element, Optimization
Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the...
Topics: Concrete evaporation rate, Plastic shrinkage, Hot weather concreting, Artificial neural networks,...
This study is aimed to explore using an artificial neural network method to anticipate the confined compressive strength and its corresponding strain for the circular concrete columns wrapped with FRP sheets. 58 experimental data of circular concrete columns tested under concentric loading were collected from the literature. The experimental data is used to train and test the neural network. A comparative study was also carried out between the neural network model and the other existing models....
Topics: Concrete columns, CFRP, Confinement, Artificial neural networks, Models
Rocks were subjected to the deformation test under five different confining stresses (0, 5, 10, 15, and 20 MPa) using the Hoek cell to determine changes in the elastic properties of the rocks under confining stress, and the results were evaluated based on density, porosity, Schmidt hardness, and compressive strength. A total of nine different rocks, two granites, two andesites, two limestones, one tuff, one diorite, and one marble, were used. When the confining stress was increased from 0 MPa...
Topics: Triaxial stress state, Confining stress, Modulus of elasticity, Modeling, Gene expression...
Precipitation forecasting is of great importance in various aspects of catchment management, drought, and flood warning. Precipitation is regarded as one of the important components of the water cycle and plays a crucial role in measuring the climatic characteristics of each region. The present study aims to forecast monthly precipitation in Semnan city by using artificial neural networks (ANN). For this purpose, we used the minimum and maximum temperature data, mean relative humidity, wind...
Topics: Monthly precipitation, Precipitation forecasting, Artificial neural network, Semnan
Presently, the mesh embedment in masonry is becoming a trendy research topic. In this paper, the mesh embedded masonry prism was cast and tested. The experimental data were used for the analytical modelling. Compressive strength (CS) test was conducted for forty five masonry prism specimens with and without poultry netting mesh (PNM) embedment in the bed joints. The small mesh embedment in the masonry prism provides the better strength improvement as well as the endurance. The size of masonry...
Topics: Masonry, PNM mesh, Compressive strength, Analytical modelling, Statistical Artificial neural...
The recent alternation of urban hydrology is seen significant due to the growth of urban sprawl. In the changed urban hydrology and urban settings, the city drainage is seen underperformed and problems are manifolds. This study therefore aims to evaluate the hydrologic performance of drainage under different land use patterns demonstrating urbanization effects using the Mahesh Khal in Chattogram as a studied watershed. This study analyses land use pattern of the study area with the data...
Topics: ArcGIS, HEC-HMS, CN, Runoff, Manning’s n
Pervious Concrete (PC) have continued gaining acceptability significantly over years due to its sustainability and environmentally friendly. There are many benefits of using PC, some of which includes management of storm water runoff, groundwater supplier recharging and reduction of heat island effects etc. Numerous studies have been carried out through employing different approaches in order to improve the overall performance of PC. Due to the advancement in high performance PC using...
Topics: Pervious concrete, Rice husk ash, Calcium carbide waste, Compressive strength, Tensile strength,...
Coir and Sisal are agriculture wastes that are effectively and financially accessible in the distinctive piece of Karnataka and other various states of republic India. These are generally treated as bio-compostable material by the customary horticulture/agriculture professionals. Aftereffects of past research related to fabrication, testing, analysis and design of conventional (synthetic fiber reinforced) composite materials portray that, strength to weight proportion is the basic criteria for...
Topics: Tensile strength, Flexure strength, Impact strength, Polymer matrix, Plant fibers
In water infrastructures design problems, small changes in their geometries lead to a major variation in the construction time and costs. Dams are such important water infrastructures, which have different types regarding their materials and their behavior to endure loads. In the current paper, invasive weed optimization (IWO) algorithm is employed to find the best shape of a concrete gravity dam (Tilari Dam, India). Stress and stability were considered as design constraints, based on the...
Topics: Concrete gravity dams, Optimum design, Nature-inspired algorithms, Invasive weed optimization (IWO)...
Reservoir operations need computational models that can attend to both its real time data analytics and multi-objective optimization. This is now increasingly necessary due to the growing complexities of reservoir’s hydrological structures, ever-increasing its operational data, and conflicting conditions in optimizing the its operations. Past related studies have mostly attended to either real time data analytics, or multi-objective optimization of reservoir operations. This review study,...
Topics: Multi-objective optimization, Reservoir operations, Real time recurrent learning Neural Network,...
Detection and prediction of cracks play a vital role in the maintenance of concrete structures. The manual instructions result in having images captured from different sources wherein the acquisition of such images into the network may cause an error. The errors are rectified by a method to increase the resolution of those images and are imposed through Super-Resolution Generative Adversarial Network (SRGAN) with a pre-trained model of VGG19. After increasing the resolution then comes the...
Topics: Generative adversarial network (GAN), Crack prediction, Super-resolution generative adversarial...
There are different kinds of intensity measures to characterize the main properties of the earthquake records. This paper proposes a simulation-based approach to compute correlation coefficients of motion duration and intensity measures of the earthquake ground motions. This method is used to investigate the influence of the ground motion data set selection in resulting duration-intensity correlation coefficients. The simulation procedure is used to tackle the problem of inadequate available...
Topics: Earthquake ground motion, Intensity measure, Strong ground motion duration, Statistical correlation...
Compression index (Cc) of normally consolidated (NC) clays determined by the oedometer experiments is utilized for calculating the consolidation settlement of shallow foundations. The determination of the Cc from the tests takes much more time and so empirical correlations based on clay properties can be a suitable alternative for the prediction of settlement. However, uncertainty in the measurements of input parameters has always been a major concern. Robust optimization is very popular due to...
Topics: Compression index, Saturated clays, Consolidation settlement, Robust optimization, Second order cone
Inaccurate cost estimates have substantial effects on the final cost of construction projects and erode profits. Cost estimation at conceptual phase is a challenge as inadequate information is available. For this purpose, approaches for cost estimation have been explored thoroughly, however they are not employed extensively in practice. The main goal of this paper is to comparing the performance of various models in predicting the cost of construction projects at early conceptual phase in the...
Topics: Cost prediction, Highway construction project, Neural networks, Support vector machine, Random...
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